Elsevier

Procedia CIRP

Volume 84, 2019, Pages 992-997
Procedia CIRP

Significance and Challenges of Data-driven Product Generation and Retrofit Planning

https://doi.org/10.1016/j.procir.2019.04.226Get rights and content
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Abstract

One of the notable drivers of the fourth industrial revolution is the collection of vast amounts of data along the entire lifecycle of a product. The analysis of product lifecycle data in conjunction with product hypotheses leads to promising potentials in strategic product planning. In this thesis paper, we postulate the need for data-driven product generation and retrofit planning as an interdisciplinary field of research. We define and analyze the key concepts and derive requirements in a structured way. Based on an exhaustive research of existing approaches, we structure open research questions and propose a roadmap in order to shape future research efforts.

Keywords

Data-driven Product Planning
Industrial Data Analytics
Lifecycle Analytics
Generation
Retrofit Planning

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